The genetic basis of variation in immune defense against Lysinibacillus fusiformis infection in Drosophila melanogaster

The genetic causes of phenotypic variation often differ depending on the population examined, particularly if the populations were founded by relatively small numbers of genotypes. Similarly, the genetic causes of phenotypic variation among similar traits (resistance to different xenobiotic compounds or pathogens) may also be completely different or only partially overlapping. Differences in genetic causes for variation in the same trait among populations suggests context dependence for how selection acts on those traits. Similarities in the genetic causes of variation for different traits, on the other hand, suggests pleiotropy which would also influence how natural selection shapes variation in a trait. We characterized immune defense against a natural Drosophila pathogen, the Gram-positive bacterium Lysinibacillus fusiformis, in three different populations and found almost no overlap in the genetic architecture of variation in survival post infection. However, when comparing our results to a similar experiment with the fungal pathogen, B. bassiana, we found a convincing shared QTL peak for both pathogens. This peak contains the Bomanin cluster of Drosophila immune effectors. Loss of function mutants and RNAi knockdown experiments confirms a role of some of these genes in immune defense against both pathogens. This suggests that natural selection may act on the entire cluster of Bomanin genes (and the linked region under the QTL) or specific peptides for specific pathogens.


Introduction
Infection is ubiquitous, but the ways in which different organisms fight infection with different pathogens are likely as varied as the organisms themselves. Within species, there is often enormous genetic variation in the ability to survive infection and it is not clear whether the same host variants will provide wide ranging protection against several pathogens or whether specific host alleles provide narrow protection against one or a few pathogens [1][2][3][4][5][6][7][8][9][10]. If certain host alleles are indeed generally better at fighting infection, it follows that those alleles must either be increasing in frequency on their way to fixing in the population or that their fitness benefits are context dependent. Perhaps alleles conferring resistance are energetically more costly or cause dysbiosis of the native microbiota [11][12][13].
Drosophila and other invertebrates lack an adaptive immune system, which allows us to focus on how the innate immune system functions in infection and disease [14]. As a result, some of the initial insight about immune defense pathways were gained by studying Drosophila and such advances continue today [14][15][16][17]. Pioneering work in the 1980s provided insight into the Toll and Imd pathways which are largely conserved in vertebrates [18]. Recently, researchers have worked out more specific associations between immune effectors and infection outcomes [5,19,20]. In addition to the functional genetic work on the immune system, others have focused on the role of natural genetic variation in resistance to infection [1,[21][22][23]. In some cases, those studies have inspired a deeper functional genetic dissection of the roles of specific immune genes in infection [24].
Insect innate immunity can be broadly grouped into cellular and humoral immunity. Cellular immunity involves the differentiation of blood cells that have several roles in directly attacking and/or immobilizing pathogens [25]. In contrast, the humoral immune response involves signaling cascades that generally lead to enormous induction of immune effectors [26]. In Drosophila, the canonical view is that the Toll pathway is activated in response to Gram-positive and fungal infection and stimulates expression of immune and antimicrobial peptide genes (Bomanins, Daishos, Drosomycins, etc.) [14,27,28]. This canonical view suggests that the Imd pathway is activated in response to Gram-negative infection and stimulates the expression of antimicrobial peptides (Diptericins, Cecropins, Defensins, Attacins, etc). Other pathways including JNK and JAK/STAT also play important roles and likely interact with Toll and Imd [29,30].
Dissecting the genetic bases of phenotypic variation in a phenotype such as immune defense yields two important results that are fundamentally different than candidate gene knockout approaches. First, because the associated approaches are unbiased, they often reveal genes previously unknown to be involved in immune pathways. Second, an understanding of the genetic causes of phenotypic variation informs both genetics and evolution. There are two commonly used D. melanogaster panels used for mapping genetic variation in phenotypes. The first is the Drosophila Genetic Reference Panel (DGRP) which was derived from a set of gravid females collected at a farmer's market in North Carolina, USA [31]. The offspring of those gravid females were inbred for several generations, then 205 lines were sequenced and made available to the public. This allows for a standard genome-wide association study (GWAS) approach to determining the genetic basis of phenotypic variation and has been used widely for several different phenotypes [32][33][34][35][36][37], including response to infection [21,38,39]. The second panel is the Drosophila Synthetic Population Resource (DSPR) [40,41]. This set of lines (actually, two mostly independent sets) were derived from eight inbred founder lines that were allowed to interbreed for several generations, then were inbred. Founders were sequenced to high depth and the recombinant inbred lines (RILs) were genotyped at low depth to assign founder genotypes across the genome. This approach allows a quantitative trait locus (QTL) approach [42,43]. The DSPR lines have not been used for studies of genetic variation in infection, except for the fungal insect pathogen Beauvaria bassiana [44], Drosophila Sigma Virus [24], and Drosophila C Virus [45]. Mapping genetic variation for resistance to infection (or any trait) requires that there be variants at high enough frequency in the mapping population to have statistical power to see an effect. These three mapping panels are derived from three pools of segregating alleles and whether the variants that influence susceptibility to infection are likely to be present in multiple panels depends on the frequency of those variants in natural populations, and in the case of the DSPR, the extent to which they are represented in the recombinant inbred lines. Therefore, a locus that shows significant association in one panel but little or no association in another panel may lack sufficient genetic variation at that locus in the second panel to find a significant association. This does not mean that the locus is unimportant for the trait in that population, just that the genetic variation is insufficient to show an association.
Our study investigates natural variation in defense against the Gram-positive Lysinibacillus fusiformis, which was isolated from Drosophila in the lab [46]. L. fusiformis is the first member of the Bacillaceae family to be used in a mapping study in Drosophila. It is an outlier for bacterial infection because it contains lysine, alanine, glutamic acid, and aspartic acid in its peptidoglycan instead of the standard Diaminopimelic acid (DAP) in most Gram-negative bacteria or l-lysine-(Lys) for Gram-positive bacteria [47]. It is a generalist, found associated with human respiratory illness [48], in symbiotic relationships with ticks [49], and in Drosophila stocks [46]. We measure survival 2-and 5-days after septic injury with L. fusiformis in the DGRP population and both DSPR populations (A and B). We also perform RNA-sequencing of flies 12-hours post infection and find that induction of gene expression after infection with the Gram-positive L. fusiformis is better correlated with induction of gene expression after Gramnegative microbes than with other Gram-positive microbes. This may be because the peptidoglycan, which is responsible in part for immune induction, of L. fusiformis is not similar to that of most Gram-positive microbes. Though the DGRP analysis reveals an excellent candidate gene for susceptibility to L. fusiformis, we find very little overlap in significantly associated SNPs with other immune-related studies. In contrast, the DSPR A population has a QTL peak that overlaps with a single peak in the B. bassiana study. While this peak contains several genes, we explore the possibility that variation in the same genes is responsible for infection differences for both L. fusiformis and B. bassiana. Our study highlights the importance of having relatively high frequency genetic variants to be able to draw conclusions about similarities among mapping studies, and the likelihood that haplotypes may have similar susceptibilities to very different pathogens.
needle survived (Fig 1A). This represents a significant, but moderate reduction in survival (Cox Proportional Hazard test, hazard ratio = 16.1, P = 0.007). Note that to achieve sensible hazard ratio, we called one sterile pricked fly dead at day 7. At this point, we could have elected to use a higher dose for subsequent infection experiments, but the OD 600 around 4.0 led to sizable variation in survival among different lines (both DGRP and DSPR had lines with survival rates close to zero), so we continued using this dosage throughout the remainder of the experiment.
We performed RNA-sequencing of pools of males 12 hours post-infection with L. fusiformis to examine changes in gene expression upon infection (Fig 1B). The most highly induced genes are expected based on other studies [50][51][52] and are mostly antimicrobial peptides in the Toll and Imd pathways. We used an adjusted P-value threshold of 0.1 for Gene Ontology enrichment analysis, which yielded 36 genes differentially expressed. Gene Ontology categories significantly enriched included several related to immune defense: response to bacteria, response to external biotic stress, defense response, immune response, defense response to Gram-positive bacterium, defense response to Gram-negative bacterium (see the entire list in S1 Table).
It appears that the induced antimicrobial peptides are more squarely induced by the Imd pathway instead of the Toll pathway, suggesting induction of gene expression is more like Gram-negative bacteria than Gram-positive bacteria. The Lysinibacillus genus are Gram-positive bacteria, but like others in the Bacillales order, the peptidoglycan is not l-lysine-(lys) type. Instead, most Lysinibacillus examined have the A4alpha (Lys-Asp) peptidoglycan [53,54]. Since the humoral response in D. melanogaster is driven in part by recognition of peptidoglycan type [55,56], the expected relative responses of the Toll and Imd responses to Bacillales is less clear cut than for most bacteria that fit more neatly into Gram-positive and Gram-negative categories based on peptidoglycan [14]. To determine whether the transcriptional response to L. fusiformis infection is indeed more "Gram-negative-like", we calculated Pearson correlation coefficients for Log 2 Fold Change (infected vs. uninfected) between our L. fusiformis data and 12-hour post infection gene expression for 11 bacterial infection treatments from the Troha et al. [50] data. Although all correlations were significant (P<0.0001), there was stronger correlation between L. fusiformis and Gram-negative bacteria (median = 0.4) than any of the Gram-positive bacteria (median = 0.32, Fig 1C). The same pattern is true when we only considered significantly differentially expressed genes (correlations for Gram-positive range from 0.798 to 0.832 and for Gram+ range from 0.910 to 0.949) or only significantly induced genes (correlations for Gram-positive range from 0.560 to 0.653 and for Gram+ range from 0.786 to 0.903), and these correlations are slightly stronger if we compare induction after L. fusiformis infection to the average induction after Gram-positive infection (Pearson correlation coefficient = 0.336) or Gram-negative infection (Pearson correlation coefficient = 0.433). Thus, L. fusiformis is a moderately virulent bacterial pathogen that induces a strong innate immune response that is more like the transcriptional response to Gram-negative than Gram-positive bacteria.

Genetic variation for immune defense against L. fusiformis in the DGRP
We aimed to map the genetic causes of variation in resistance to infection in three (DGRP and two DSPR) populations. All three populations showed genetic variation for survival after infection with L. fusiformis (Figs 2A, 3A and 4A). Raw survival in the DGRP population ranged from 10% to 100% surviving 5 days post infection (DPI). We also examined survival 2 days post infection, which was much higher overall and showed less variation (S1 Fig). Like several other studies of immune defense in the DGRP, we found that line 321 was particularly  For asspciations in the DGRP, we used the online DGRP mapping tool because it accounts for inversions, Wolbachia infection status and the relatedness of lines. We performed the

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Defense against Lysinibacillus in D. melanogaster and other lines by chance (and not genetic linkage) will show inflated significance. In contrast, the omitting line 321 and examining survival 5 days post infection yields a QQ plot that mostly fits expectations with a few outliers that are more significant than expectations.
The FDR-adjusted significance threshold of 0.1 yielded only a handful of significant SNPs (S3-S6 Tables). Sixteen SNPs were significantly associated with survival 5 DPI when all lines were considered. Eleven intronic SNPs were in eys/CG9967, Pvr, CG44153, Trim9, CG15611 (4 SNPs), trio, CG32698 (actually a 2bp deletion), and Frq1. One SNP was in the exon of the noncoding RNA CR44310, one SNP caused a silent change in CG14971, and 3 SNPs were intergenic. When we omitted line 321, only 3 SNPs were significantly associated with 5-day survival-all three were intronic SNPs in Pvr. Note that the SNPs significantly associated when all lines were included but not significantly associated when 321 was omitted were mostly (13 out of 14) still tested but had low enough minor allele frequencies that power was lost and Pvalues were above our significance threshold. Eight SNPs were significantly associated with 2-day survival when all lines were considered. This included 4 intronic SNPs in Zasp52, trio, CG32364, and Plod as well as 4 SNPs in the 3' UTR of CG15564. There were no significantly associated SNPs 2-days post infection when line 321 was omitted. The SNPs in Pvr significantly associated at 5-days post infection were nominally significant at 2-days post infection (~3.0 x 10 −4 when all lines were considered and~4.0 x 10 −5 when line 321 was omitted). With our conservative cutoff for significance, only one SNP (3L:1030175, intronic in the Rho guanine exchange factor, trio) was significantly associated at both 2-days and 5-days post infection. Similarly, only one SNP (2L:8232234, intronic in Pvr) was significantly associated at 5-days post infection whether line 321 was included or not.
We investigated genes connected to significantly associated SNPs in a few ways. First, we examined whether any of these genes were differentially expressed 12 hours after infection based on our RNA-seq data. None were significantly differentially expressed even if we consider the nominal P-values, so genes connected to significantly associated SNPs are not significantly induced or repressed upon infection at the 12-hour time point. Relying on transcriptional responses to infection assumes that genes must be induced to have a role in infection. This is clearly not always true but provides one line of corroborating evidence.
Next, we examined annotated functions of these genes. Two stand out. First, Pvr (PDGFand VEGF-receptor related) is involved in the regulation of several innate immune functions including apoptosis, humoral immune response, JNK signaling and wound healing [57][58][59][60][61]. Pvr is a negative regulator of humoral immune response, so if these SNPs are associated with alternative splicing or otherwise influence Pvr expression, it would be a plausible connection. We performed RNAi knockdowns of Pvr using ubiquitous drivers (which produced no offspring), and hemocyte and fat body drivers. Though not significant, the knockdown genotypes survived better after infection with L. fusiformis than controls for the hemocyte driver and one of the fat body drivers (S5 Fig). We used a higher dose (OD 600~1 0) in these experiments after pilot experiments suggested Pvr knockdown enhanced survival. Given that Pvr is a negative regulator of humoral immunity, the increased survival makes sense, but we hesitate to draw any conclusions from this data since the differences were not significant. The other gene of interest is CG44153, which is predicted to enable cell-cell adhesion mediator activity and is active in the plasma membrane [62]. It stands out because it was also identified in two other DGRP immunity association studies: survival time after infection with Pseudomonas aeruginosa [21], and microbiota-related diet shift [63]. However, CG44153 also was reported to harbor genetic variation associated with several other non-immune phenotypes, so it is possible that there is something about genetic variation at the locus that makes it prone to false positive associations.

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Defense against Lysinibacillus in D. melanogaster

Little evidence for correlations among immune phenotypes in the DGRP
The DGRP lines have been available to the public for more than a decade, and in that time, there have been several studies attempting to map variation in immune defense against different pathogens. This allows us to begin to determine whether immune defense against different pathogens is correlated. Are lines that are better at fighting infection against one pathogen, generally better at fighting infection? If so, is this true universally or within categories such as Gram-positive bacteria? A general correlation in immune defense would suggest that at least some of the genetic basis of that variation is also shared for these different pathogens. There is some evidence for this in the overlap in associations in this study and those of Chapman et al. and Wang et al. [1,21]. However, it is also possible that resistance is much more specific for each pathogen. This is likely given some of the specificity of associations-particularly for Providencia rettgeri and Diptericin [5,20,38].
We calculated Spearman's rank correlations for 14 DGRP immune-related data sets plus an additional 6 non-immune phenotypes ( Fig 2C). Though there are several positive correlations, few are significantly correlated after Benjamini & Hochberg correction. Correlations tend to be between the same microbe using different experimental design (different sex or survival vs. bacterial load) or between different microbes measured in the same laboratory (E. faecalis and L. fusiformis were both measured in our laboratory at the same basic time; M. anisopliae and P. aeruginosa were reported as part of the same experiment in Wang et al. [21]). However, there is an almost complete lack of significant correlation between immune phenotypes and any of the other phenotypes, the exception being a positive correlation between female survival after P. aeruginosa and male starvation survival. Given the general lack of correlation between phenotypes, we decided not to look at the overlap in significantly associated SNPs between immune phenotypes. Most of the phenotypes we chose utilized nearly all DGRP lines (170-200 of the 205 DGRP) lines. However, the number of pairwise observations for some of these correlations might be small enough that power to detect significant correlations is reduced. Differences in the lines used in different experiments also enhances the likelihood that traits measured in the same (or related) labs show correlation since those labs are likely to use a similar set of lines. Nevertheless, Pseudomonas infection phenotypes (n = 81 for P. aeruginosa) were correlated with each other and with an enteric Pseudomonas entomophila infection. Our correlation results suggest that the DGRP immune phenotypes that have been reported do show some correlation, but it is difficult with the available data to determine if these correlations are due to phylogenetic relatedness of the microbes used or are an artifact of something about the experimental design (how phenotypes were defined, dose, overlap in lines utilized, etc.) or labs where the experiments were performed.

Genetic variation for immune defense against L. fusiformis in the DSPR
The DSPR consists of two populations (A and B) that were derived from eight different founder genotypes. Each of those populations was further split into two parallel sets (e.g. A1 and A2) which were derived from the same founders, but different interbreeding populations. We used the same logistic regression model as described above to calculate line effects for each DSPR population A line (S7A Fig and S7 Table), and used the DSPRqtl package [64] to determine regions of the genome associated with variation in immune defense against L.

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Defense against Lysinibacillus in D. melanogaster fusiformis. We determined a significance threshold (alpha = 0.05) by permuting the data for 1000 iterations. This yielded several QTL peaks with LOD scores above the threshold, but they concentrate in two regions (Fig 3B). The first peak (qtl A5.1) was centered at Chr2R:3,420,000 (LOD drop 2 range 3,360,000 to 3,470,000, note this is release 5 of the D. melanogaster genome, LOD = 8.59) and was associated with 10.96% of the variation in the phenotype. We extracted the effects of individual founder genotypes (S8A Fig) and found that founder A1 genotypes at this region had significantly higher survival than the other four genotypes represented. This includes all or part of 21 genes, none of which have obvious immune function, nor were any differentially expressed in our RNA-seq data after p-value correction. One gene, Cyt-b5 showed an almost 50% reduction in expression upon infection, leading to a nominal p-value of 0.02. Importantly it is annotated to be involved in metabolism of xenobiotic compound, but the naïve expectation if it is breaking down microbial toxins would be induction, not suppression upon infection.
The second peak (qtl A5.2) had a higher LOD score and was also much broader. We focused on the main peak (highest LOD score) which was centered at Chr2R:14,720,000 (LOD drop range from 14,680,000 to 14,770,000, LOD = 21.19) and was associated with 24.89% of the variation in phenotype. This peak contains all or part of 19 genes including two genes involved in immune defense: DptA and DptB. The founder genotype from A5 is associated with significantly lower survival than the other represented genotypes (S8B Fig). If we expand to look at genes under the three adjacent (actually overlapping) peaks with LOD scores above 20, we also include the gene, Jabba, which functions in immune defense. One more QTL peak still overlapping these but with a LOD score of 14.7 (peak 20) contains the Bomanin gene cluster (eight small, secreted peptides involved in immune defense) as well as Imd (adaptor protein for the Imd pathway). Line A5 harbors two unique nonsynonymous SNPs in Jabba exon 6 as well as a loss of stop codon in BomT2 (a Bomanin). Line A2 (not susceptible) contains a 1748bp insertion of a Jockey element in the intron of BomS4 (the intron is 64bp in the other lines). The two Diptericins are tempting candidates, but neither have any variation unique to line A5 in either coding or regulatory regions. Furthermore, the DGRP lines segregate for null alleles of DptA and those lines are right in the middle of the line effects (51 st and 53 rd percentile). There is also a single line with a null mutation (premature stop) in DptB in the DGRP (line 223) and it showed 100% survival. In the DSPR, line A3 has a 37bp deletion that starts in exon 1 of DptB and ends in the intron, and that genotype has no apparent deficit in immune defense against L. fusiformis. We also investigated variation in other genes with known immune functions. Prophenoloxidase gene, PPO1, is involved in the melanization reaction. While line A5 (susceptible DSPR line) did have a single amino acid difference compared to other lines (a conservative arginine to lysine at the 420 th residue), line A7 includes single base deletion leading to a frameshift and a premature stop codon only 44 amino acids into the otherwise 690 residue protein. We chose not to investigate PPO1 further since prior work showed no effect of null PPO1 mutants except in response to infection with Staphylococcus aureus [65,66]. Finally, we examined the coding sequence of Imd and found only a single amino acid difference (A2 has a glycine to serine change at residue 103 compared to the others) among any of the four lines (A2, A5, A6 and A7) with representation at QTL A5.2.
Line effects and mapping in the DSPR A2 population were correlated when comparing Day 2 and Day 5 survival (S7A, S7B, S7F and S8 Figs and S8 Table, Pearson correlation coefficient for line effects = 0.908, Pearson correlation coefficient for LOD scores = 0.957). While the peaks overlapped significantly, the Day 2 survival did nothing to further filter to a smaller set of candidates. We therefore utilize Day 5 survival data for population A2. We

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Defense against Lysinibacillus in D. melanogaster dev. = 0.134). There was again ample variation in survival across lines (Figs 4A and S7C and S9 Table). Heritability was 0.674 for survival 5 DPI and 0.760 for survival 2 DPI. Thus, both heritability and the range of survival post infection are higher in the DSPR A panel (Figs 3A vs 4A), which may have resulted in reduced power for mapping in the B panel. The QTL scan did not result in any significant peaks with alpha = 0.05 (Fig 4B). In fact, we had to relax alpha to 0.20 to get significant peaks. There were 3 overlapping peaks on Chr3R between 15,470,000 and 16,770,000 -a region with 145 genes (qtl B5.1). This peak explained 7.35% of the variance. There are several genes plausibly associated with immune defense. First is Stat92E, the transcription factor of the JAK/STAT pathway (not significantly induced, but expression was up 25.7%). Second are a cluster of Turandots, immune/stress effectors regulated by JAK/STAT. Of the five Turandots under the peak, TotA, TotC and TotX were all significantly upregulated after infection and there was no expression data for TotB or TotC. The founder genotypes under these peaks show that lines B5 and B7 have significantly lower survival than the other lines (S9C Fig). Scanning through variation in these genes, we did not find any sites limited to B5 and B7 that were immediately good candidates for being causal. For panel B2, the correlation between LOD scores for Day 2 and Day 5 was lower and what we observed in population A (S9 and S10 Tables). In fact, there was an additional peak at Day 2 (qtl B2.1, S10 Fig). This peak is only significant with alpha = 0.1 and is on 3L at 2,880,000 (LOD score = 7.57, 2 LOD drop range: 2,850,000 to 3,100,000, 8.53% of variance explained). Founder line B3 genotypes had a significantly lower survival than others (S8D Fig). The peak includes 27 genes including spz-5 (a paralog of the canonical Toll pathway Spaetzle gene). These genes were not differentially expressed in the transcriptome data.
To further investigate overlap between the DGRP and DSPR populations, we looked for DGRP SNPs significantly associated with survival after L. fusiformis infection that were under the three focal QTL peaks (A5.1, A5.2 and B5.1). For the A populations, there were no SNPs that were significantly associated with survival after infection (adjusted P>0.85 where adjustment accounted for just those SNPs under the QTL peak). For the B population, there was a single SNP that, after multiple test correction was associated at p = 0.091, but with multiple corrections for the entire data set, the adjusted p-value was 1.0. We conclude that any causative genetic variation segregating in the DGRP in the regions of the three DSPR peaks was either absent or at too low frequency to result in a significant association.

Defense against L. fusiformis shares a similar genetic architecture with defense against a fungal pathogen
We reanalyzed the data from Shahrestani et al. [44], where DSPR panel A1 was infected with Beauveria bassiana (a fungal pathogen) and survival proportion was subsequently mapped. We performed the same logistic regression (described above), which is different from their approach, and used other variables (all random effects) as they described. Our heritability estimate was 0.921 which is much higher than what they estimated (0.53). We cannot examine correlations among lines for immune defense against B. bassiana and L. fusiformis because the experiments were done in different lines (set A1 vs. A2). However, we can determine whether there is significant overlap in QTLs, which would suggest a shared architecture for immune defense. Like L. fusiformis, there was significant variation in the random effects of line for survival after B. bassiana infection (S7E Fig and S11 Table). In contrast to what Shahrestani et al. [44] reported, we did find one peak significantly associated with survival after B. bassiana infection at alpha = 0.05 (Fig 5A and S12 Table). The peak centered at chromosome 2R:14,660,000 (Fig 5B, qtl Bbas.1, 2 LOD drop range 14,590,000-14,700,000, LOD score = 9.06, 13.15% of variance explained). The A5 founder genotype had significantly lower survival than other lines (Fig 5C).

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Defense against Lysinibacillus in D. melanogaster Thus, the B. bassiana peak is quite similar to the location of qtl A5.2 for L. fusiformis, even though the two experiments were performed in different subsets of the A population (Fig 5B). To test whether this overlap is more than expected by chance, we performed a Fisher's Exact Test with the fraction of physical positions with LOD score greater than the threshold score (alpha = 0.05). Day 5 survival for L. fusiformis had 289 of 11768 significant, while B. bassiana had a single position significant of 11768. That one position significant for B. bassiana was also significant for L. fusiformis, which is a significant excess over the expectation (expected overlap = 0.0246, Fisher Exact Test P-value < 0.0001). Furthermore, the founder genotype effects for both phenotypes clearly show reduced survival for the A5 genotype compared to all other genotypes present (this is true whether we match the peaks as in Fig 5C or pick the most significant peak for each genotype). The DSPR experiments lead us to conclude that different panels (DSPR A2 and B2), which have different founder genotypes, have different genetic architectures for immune defense against L. fusiformis, but in two different mapping experiments performed in two different labs with very different pathogens, there appears to be a shared genetic architecture for resistance to infection (DSPR A2 L. fusiformis vs. DSPR A1 for B. bassiana).

Infections of mutant and RNAi knockdown flies show the potential role of individual genes in immune defense against both L. fusiformis and B. bassiana
The number of genes under the shared QTL peak in the DSPR A population are too numerous (96 genes plus tRNAs and lncRNAs) to test individually. We therefore tested several reasonable candidate genes including 4 genes in the Bomanin cluster and Jabba by RNAi knockdown plus knockout lines for genes encoding all Bomanins a subset of Bomanins, Imd, and Diptericin A. The BomΔ 55C line contains an excision of all 10 Bomanin paralogs in the cluster on chromosome 2R, and the BomΔ left contains an excision of the six "left-most" paralogs: BomT1, BomBc1, BomS1, BomS4, BomBc2, and BomS2. Infections with L. fusiformis confirm a role for Bomanin genes in survival after infection since both BomΔ 55C and BomΔ left show significantly reduced survival compared to control (Fig 6A). In contrast, neither Dpt nor Imd null mutants had any effect on survival after L. fusiformis infection (Fig 6B). B. bassiana infections also revealed an important role for the Bomanin cluster as again both mutants showed severely reduced survival after infection ( Fig 6C). Interestingly, Imd null mutants also showed reduced survival after infection with B. bassiana compared to controls, suggesting a role for Imd in defense against fungal pathogens (Fig 6D).
With evidence that the Bomanin cluster was generally important for infection with L. fusiformis and B. bassiana, we sought to determine whether specific Bomanin copies had specific protective benefits against infection using fat body RNAi of individual Bomanins. After infection with L. fusiformis or B. bassiana, both BomS2 and BomBC2 knockdown flies stand out as being more susceptible to infection than controls (Fig 7, S13-S17 Tables, P<0.001 in each comparison). Other members of the Bomanin cluster had little to no influence on survival after infection with either pathogen. Given the similarity in sequence of some of these paralogs, we tested whether RNAi knockdowns were specific to specific Bomanins. While the two bicipital forms (BomBc1 and BomBc2) were specific, BomS1 did show some reduced induction even when BomS2 was targeted by RNAi (see methods). Jabba fat body knockdowns also had little impact on survival after infection. Note that the two AttP40 RFP RNAi knockdowns, used as additional controls, showed intermediate survival. Each still show significantly higher survival compared to BomBc2 (P<0.001) for both L. fusiformis and B. bassiana. Taken together, it appears that specific Bomanin paralogs (BomS2 and BomBc2) provides some defense against L. fusiformis and B. bassiana infection. However, the best approach would be confirmed CRISPR

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Defense against Lysinibacillus in D. melanogaster knockouts of each specific Bomanin paralog, or even better, recreation of the specific mutations observed in the DSPR lines.

Discussion
Populations face infection challenge from innumerable different pathogens. The genetic factors that influence susceptibility to infection may vary from population to population for a

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Defense against Lysinibacillus in D. melanogaster specific pathogen, and from pathogen to pathogen within a specific host population. We examined genetic variation for susceptibility to the Gram-positive bacterium, L. fusiformis infection in three Drosophila mapping populations and find no evidence that the same genetic factors contribute to phenotypic variation in susceptibility across the three populations. We also

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Defense against Lysinibacillus in D. melanogaster compared the genetic causes of phenotypic variation for susceptibility to L. fusiformis and the fungal pathogen, B. bassiana [44]. Surprisingly, even though these experiments were conducted by different researchers in different labs with different subpopulations, there was striking concordance of a single quantitative trait locus (QTL) for both pathogens. Although that QTL peak was broad, it contains a cluster of immune peptides, some of which are induced upon infection, and show increased susceptibility to both infections upon RNAi knockdown.
We hoped to determine the extent to which different genotypes are correlated in how they defend against infection. Are some genotypes universally more resistant to infection? Is correlation stronger when pathogens are more closely related? Do correlations suggest tradeoffs among immune phenotypes (pathogen X vs. pathogen Y) or between immune phenotypes and other life history traits? The DGRP lines are widely used for measuring phenotypic variation and attempting to map the genetic causes of that variation. As such, there are several studies on both immune and life history traits to examine genotypic correlations. Although we did find evidence of correlation, it was difficult to tease apart whether these relationships were truly biological or were artifacts-stronger correlations tended to be between phenotypes measured in the same lab or institution (different pathogens in the same lab, the same phenotype for each sex). In fact, there were no significant negative correlations, which would be expected with strong tradeoffs. The only exception to the general lack of signal (positive or negative) was the positive correlation between male starvation stress resistance and female P. aeruginosa survival. Although this might suggest that some genotypes are better at responding to general stress (starvation or infection) than others, it is curious that the correlation is only significant for one infection type and not even in males infected with P. aeruginosa. We therefore hesitate to draw conclusions from this correlation.
In contrast to the DGRP experiments, we find a striking shared QTL peak comparing infection with both L. fusiformis and B. bassiana in the DSPR lines. We are unable to measure the correlation between genotypes since the two experiments were performed in two different DSPR subpopulations. Note that both subpopulations were initiated from the same set of eight founder genotypes, so the genetic variation present in the two subpopulations is similar. The shared peak for two very different pathogens could mean several things. First, there are many genes under that peak and even many paralogs of the genes encoding Bomanin peptides. Therefore, it is possible that the genetic basis of increased susceptibility to L. fusiformis in the A5 background is different from the genetic basis of increased susceptibility to B. bassiana in the A5 background. If the phenotypic variation for the two pathogens was caused by different, and unique genetic changes, we imagine that natural selection might act on these linked variants as a single unit. Second, there may be a shared genetic basis for resistance to infection. Perhaps a single Bomanin (or small subset of Bomanins) are responsible for most of the response to infection, but in a non pathogen-specific way. At this point, we know very little about how specific Bomanins act in the immune system (Table 1), so whether individual Bomanin paralogs act generally or broadly is not clear. If single Bomanins or even clusters of Bomanins act generally, times of high pathogen pressure should select for resistant (either due to coding variation or higher expression) genotypes. Such selection would be relaxed or reversed (if Bomanins are costly) in the absence of infection.
Our approach enabled us to also determine whether the same genetic factors influence immune defense in different populations. We acknowledge that none of the three populations are ideal populations: DGRP are inbred lines that survived the lab environment after capture at a farmer's market, DSPR are derived from several lines from around the world and made into a "synthetic" population. Nonetheless, there was no evidence that any of the three populations shared genetic variation in any particular region that influenced survival after L. fusiformis infection. This may be because pathogen pressure varied in the populations from which

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Defense against Lysinibacillus in D. melanogaster these lines were derived. It may also be that immune alleles tend to turn over rapidly as populations coevolve with pathogens meaning that the alleles best suited to fight infection in one population are missing from another population. Finally, the small founder populations in the DSPR may preclude anything but high frequency variants from being detected in multiple experiments.
Several recent studies have attempted to characterize the role of individual Bomanin genes, the entire cluster of 10 genes, or subsets of those genes (summarized in Table 1) [1][2][3]27,50]. This began with Clemmons et al. [27] who characterized the gene family and showed that deletion of the entire cluster rendered flies highly susceptible to infection with E. faecalis, C. glabrata and F. oxysporum, but not the Gram-negative E. cloacae. The authors also showed the first evidence that there might be some specificity of activity since deleting the six left paralogs of the Bomanin cluster resulted in high susceptibility to E. faecalis and F. oxysporum, but not C. glabrata. To investigate further, Lindsay et al. [2] infected flies with four Bomanin transgenes on a Bomanin cluster partial deletion with C. glabrata. Two transgenes (BomS3 and BomS5) restored survival to wildtype levels, one transgene (BomS6) showed intermediate restoration of survival, and one (BomT2) showed no restoration of wildtype survival. These experiments show that BomS3 and BomS5 are not just necessary, but sufficient to protect against infection with C. glabrata (at least among the Bomanins). As expected, the expression of these genes after infection correlates with their ability to restore wildtype survival. When expressed with the BomS3 promoter, BomS6 was not susceptible to C. glabrata infection, but BomT2 showed no difference in susceptibility. Xu et al. [3] showed that BomBc1, BomS3 and BomS4 were significantly induced upon A. fumigatus infection and that, as a whole, the Bomanin cluster enhances both resistance to, and tolerance of, infection with A. fumigatus. BomBc1, BomS3 and BomS6 increased tolerance to the fungal toxin, restrictocin, whereas Bom S6 and BomS1 increased tolerance to another toxin, verruculogen.
Here, we showed that natural variation in immune defense against both the Gram-positive L. fusiformis and the fungal B. bassiana map to a QTL that contains the Bomanin cluster and

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Defense against Lysinibacillus in D. melanogaster that knockouts of the entire cluster or the left six copies and knockdown of specific Bomanin copies result in enhanced susceptibility (note that we do not differentiate between tolerance and resistance). Thus, our study adds to the growing evidence for the importance of the Bomanin cluster in immune defense, but also shows that naturally occurring genetic variation is associated with variation in immune defense against two different pathogens. Unfortunately, most of these studies utilize a subset of the Bomanins and use different techniques (CRISPR/ Cas9 knockouts, transgenic lines, RNAi), so a systematic understanding of the specificity of individual Bomanins or the subclasses (tailed, short and bicipital) remains elusive, but would be necessary to truly understand paralog specificity. This study highlights the context-dependence of the causes of genetic variation in immune defense. Within the same species, different populations harbor different genetic variants that result in variation in immune defense. Surprisingly, however, within a population, two very different pathogens share a QTL for susceptibility to infection, with a single founder genotype being significantly more susceptible than others. Although our approach does not have the resolution to determine whether the causal genetic variants are the mutation for both L. fusiformis and B. bassiana or are just genetically linked, given the high effective rate of recombination in D. melanogaster overall and that this region experiences exceptionally high recombination [67,68], there is a reasonable chance that the same mutation underlies both immune phenotypes. This linkage may allow natural selection to act more broadly to improve infection outcomes when susceptibility to such disparate pathogens is governed by a single locus.

Drosophila strains and husbandry
Drosophila Synthetic Population Resource (DSPR) lines from populations A1 and B1 are maintained by Stuart Macdonald at the University of Kansas [40]. The DSPR population lines were derived from two different sets of founder inbred lines. Each population was interbred among lines for several generations, then inbred to create the recombinant inbred lines used in these experiments. Drosophila Genetic Reference Panel (DGRP) lines were obtained from Brian Lazzaro at Cornell University and from the Bloomington Drosophila Stock Center (Bloomington, IN). Canton S is a standard laboratory Drosophila line. All Drosophila stocks were maintained on standard molasses media at 23 degrees Celsius on a cycle of 12 hours light to 12 hours dark. We refer to both sets of lines by their numeric assignment (for the DGRP we refer to line 321, not RAL_321 or line_321).

Microbes and microbiology
Lysinibacillus fusiformis (strain Juneja) is a natural pathogen of Drosophila obtained from Brian Lazzaro at Cornell University [46]. Bacteria were grown from single colonies in LB liquid media overnight to stationary phase, then concentrated to an optical density (600nm) of about 4.0. Beauvaria bassiana (strain GHA) was obtained from Parvin Shahrestani (California State University at Fullerton). Spores were grown in malt extract agar, then resuspended with a 10ml syringe, filtered through a 0.22 μm Miracell filter and resuspended in 20% glycerol at 4.3e08 spores per mL as measured on a hemocytometer.

General infection protocol
Flies were tipped to new media and cleared after 2-4 days of egg laying to control larval density. Ten to fourteen days later, emerging adults were moved to new media vials. Three-to seven-day old males were infected after light CO 2 anesthesia by pricking in the thorax with a

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Defense against Lysinibacillus in D. melanogaster needle dipped in bacteria or fungal suspension or LB broth (or 20% glycerol) as a sterile control. The number surviving 2-and 5-days post infection was noted. For Canton S infections and RNAi knockdowns, we noted survival daily. Infections were performed over several weeks with 3-4 days of infection each week. A single researcher performed all infections (n = 22174) for the GWAS and QTL experiments, and a different researcher performed all infections for the RNAi experiments. These experiments were done in batches of subsets of lines since it would not be possible to infect all lines in one batch. Each subset of lines was chosen randomly and batch effects were included in statistical models.

Infection dynamics in Canton S
The effect of L. fusiformis infection on Canton S survival was assessed using a Cox Proportional Hazard test implemented in the R packages survival and survminer [69,70] with treatment (infected vs. sterile prick) as the independent variable.

RNA-sequencing of infection in Canton-S males
We used Canton S flies to compare expression 12 hours after infection with L. fusiformis to those uninfected (no sterile prick). Each treatment contained 3 replicates of 3 flies per replicate. We flash froze flies in liquid nitrogen, then extracted RNA using a Zymo Quick-RNA Microprep kit (R1050, Zymo Research, Irvine, CA). We then constructed RNA libraries using the Lexogen QuantSeq 3' mRNA-Seq Library Prep kit (Lexogen GmbH, Vienna, Austria). We sequenced the samples on an Illumina MiSeq Micro SR100 run. Raw Reads were quality filtered using the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/; RRID:SCR_005534), then mapped with STAR version 2.7.10b [71,72] to the Drosophila melanogaster (r6.18) genome, processed with samtools version 1.9 [73,74] and analyzed with DESeq2 [75] using a simple infection vs. no prick design. We performed the DGRP genome wide associations with and without line 321. We used both approaches because line 321 showed unusually low survival at 5 days post infection (DPI) and others [1,21] noted and excluded 321 in their analysis of resistance to Metarhizium anisopliae and Enterococcus faecalis infections. However, line 309 actually had lower survival (0% at 5 DPI) and we hesitate to start dropping all lines with low survival since variation is the point of this type of mapping. We measured survival both 2-and 5-days post infection and performed the mapping experiment at both time points. We fit a mixed effects logistic regression model using the lmer function in lme4 [76] and line effects were extracted using the ranef function. These values were used with the DGRP website [31] to perform association mapping. Thus, there were four total mapping experiments for the DGRP: 2-and 5-days post infection and with and without line 321. We calculated heritabilities using line variance and total variance from the VarrCorr function in lme4.

Genetic variation for immune defense in the DGRP
We next asked whether line effects correlated between DGRP infection experiments (and a few other traits). We retrieved line effects for both male and female 24 hour bacterial load and female survival post Providencia rettgeri infection [5,38], male and female Coxiella burnetii hazard ratio [77], female Pseudomonas entomophila enteric survival [78], male and female Pseudomonas aeruginosa survival [21], male and female Metarhizium anisopliae survival [21], Staphylococcus aureus phagocyte mobilization [79], Listeria monocytogenes bacterial load [80],

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Defense against Lysinibacillus in D. melanogaster Enterococcus faecalis survival [1], male aggression [81], male starvation resistance [31], pigmentation (tergite 5) [82], lifespan and fecundity [83], and weight [35]. We calculated Spearman's rank correlations for these 20 phenotypes and grouped by non-immune, Grampositive/fungal and Gram-negative pathogens. In these analyses, we used the negative of the load values which means that all immune phenotypes have higher values indicating better immune defense and lower values meaning worse immune defense.

Genetic variation for immune defense in the DSPR
The DSPR experiment was similar to that for the DGRP, but with more lines and fewer individuals infected per line. We used from one to four vials (usually 2-3 vials), consisting of 8 to 10 individuals, per line in the DSPR population. This corresponds to an average of 22.3 flies infected per line in 343 lines from DSPR population A2 and 18.8 flies infected per line in 391 lines from DSPR population B2. For each population, we used a logistic regression for proportion alive at day 2 and day 5 with line and infection date as random effects.
These random effects of line were used directly in the DSPRqtl package in R [40,41]. We used the DSPRscan function with the model effect~1. To determine a significance threshold, we performed 1000 permutations using the same model. Peaks were determined using the LOD drop 2 method in the DSPRPeaks function and nearby and overlapping peaks were curated by hand.
We also retrieved raw data for survival after B. bassiana infection [44] and performed a logistic regression analysis with random effects of line, replicate nested in line, group and group nested in set (as in their analysis). This differs from the authors' approach who used arcsin-square root transformed survival. All other analyses were performed as above.

Mutant and RNAi knockdown experiments to examine the role of individual genes in immune defense
We followed up our QTL mapping by examining several genes that occur putatively associated with immune defense. These included null mutants (Imd, Diptericin A, BomΔ 55C and BomΔ left ) and their respective controls. We obtained the TRIP RNAi lines [84] from the Bloomington Drosophila Stock Center (Bloomington, Indiana, USA) and crossed to the C564 (RRID: BDSC_6982) fat body-specific driver [85]. In the case of Pvr, we used two additional drivers. We also crossed the driver to an empty vector and RFP RNAi (when available) as negative controls. All lines are described in S18 Table. Infections were performed as described above for L. fusiformis, but B. bassiana were monitored for 21 days instead of 7 days, with flies transferred to new food every 3-4 days. In addition, we mock infected lines with either Luria-Bertani broth or 20% glycerol as a sterile wound control. Very few of these flies died from the wound. We assessed significant differences in survival using the coxph function from the survival package in R [69,86] with genotype and date as variables and the empty vector as the reference. Since the RNAi lines are on different backgrounds (AttP2 and AttP40), we performed separate analyses based on background. Since these Bomanin genes are paralogous and have some degree of sequence similarity, we performed qPCR to examine the degree of specificity for each knockdown. To do so, we first determined the RNAi target sequence for each focal Bomanin gene, then found the paralog with the sequence closest to that target sequence. We then asked whether both target gene and closest paralog (based on target sequence) were differentially expressed 8 hours post infection or no-infection control and between knockdown and RNAi control (BDSC_36303 or BDSC_36304). qPCR primers are listed in S19

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Defense against Lysinibacillus in D. melanogaster induction compared to control when BomS2 was targeted. For the bicipital form Bomanins (S10B Fig), both were specific-only showing decreased induction when the focal gene was targeted. We assessed significance with a simple t test of the difference in relative expression in infected vs. uninfected conditions: (RP49 infected -target infected )-(RP49 uninfected -target uninfected ).